{"id":"W857402252","doi":"","title":"Comparing and optimizing cholesterol extraction from hydrogel and silicone hydrogel contact lens materials","year":2014,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Ocular Surface and Contact Lens","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Silicone hydrogel; Contact lens; Silicone; Self-healing hydrogels; Lens (geology); Extraction (chemistry); Materials science; Composite material; Chemistry; Polymer chemistry; Ophthalmology; Chromatography; Engineering; Medicine; Petroleum engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004931079,0.0003858265,0.0002763386,0.0005043537,0.0002103938,0.0005036132,0.000174294,0.000247389,0.0008876375],"category_scores_gemma":[0.0008143065,0.0001400008,0.0003641217,0.0004398456,0.000155557,0.000745712,0.0002385353,0.0002350009,0.0002159597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003102944,"about_ca_system_score_gemma":0.0002653292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007450808,"about_ca_topic_score_gemma":0.001879557,"domain_scores_codex":[0.9996902,0.00004705245,0.00003685273,0.00004148326,0.0001327382,0.00005175972],"domain_scores_gemma":[0.9996467,0.0001264959,0.00007705591,0.00001845862,0.0001127037,0.00001857676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004123326,0.00006292186,0.0007549776,0.0001957127,0.00001615786,0.00007182681,0.00003597138,0.0004657068,0.9895846,0.00006767159,0.00004344981,0.008288732],"study_design_scores_gemma":[0.000008470165,0.0002612056,0.001322192,0.000007267462,0.00002689227,0.00004700927,0.0000292807,0.0006074428,0.9971397,0.00001164075,0.0005324482,0.000006513604],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915984,0.003108995,0.003510043,0.0000365577,0.00002538645,0.00003613186,0.0001381614,0.00002971522,0.001516624],"genre_scores_gemma":[0.9901406,0.001929914,0.00616616,0.00003827188,0.00001198042,0.00002651428,0.0001784258,0.0000281914,0.001480074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008876375,"threshold_uncertainty_score":0.002969444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.043891238553605,"score_gpt":0.321867239012687,"score_spread":0.277976000459082,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}